Machine Learning Engineer Senior

Techstra Solutions•Dallas, TX

About The Position

The Senior Machine Learning Engineer leads the development of cross-functional, multi-platform machine learning software systems and uses technical knowledge and industry experience to design, build, and maintain technology solutions. The role applies advanced probability, statistics, and higher-level mathematics to create machine learning algorithms, build new models, and validate existing models. It requires current knowledge of machine learning best practices and emerging trends, together with an enterprise-wide perspective. The engineer performs complex application programming activities, including coding, testing, debugging, documenting, maintaining, and modifying machine learning systems. They work with Data Scientists to understand prototypes and recommend modifications. The role also encompasses production implementation and support of generative AI and LLM solutions, model design and orchestration, governance and risk management, internal client consultation, and release planning and support.

Requirements

5–7 years of related experience.
Experience implementing production solutions that use generative AI, LLMs, and agentic frameworks.
Experience using SDLC harnesses to increase individual and team productivity.
Experience with internal client consultation, requirements gathering, and technical team alignment.
Experience designing, building, orchestrating, and implementing model solutions.
Experience building solutions with model governance, risk management, and regulatory adherence in mind.
Experience supporting models deployed to production applications.
Experience with release planning, coordination, and hands-on support during release windows.
Knowledge of machine learning and generative AI, including Python, scikit-learn, PyTorch, and/or TensorFlow.
Knowledge of generative AI, LLMs, RAG, embeddings, and vector search.
Experience with agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel, or similar.
Knowledge of model evaluation, validation, prompt engineering, and model monitoring.
Knowledge of advanced probability, statistics, linear algebra, and optimization.
Experience with enterprise ML platforms such as Azure Machine Learning, Azure AI Foundry, AWS SageMaker, or equivalent.
Experience with Azure OpenAI/OpenAI APIs or comparable foundation-model platforms.
Experience with MLflow or similar experiment-tracking and model-lifecycle tools.
Experience with vector databases or search platforms such as Azure AI Search, Pinecone, Weaviate, or pgvector.
Knowledge of Git/GitHub or Azure DevOps, REST APIs, microservices, Docker, containerized deployments, CI/CD pipelines, and automated testing.
Familiarity with Jira/Confluence or similar Agile delivery tools and with SDLC practices, source control, code reviews, release management, and production support.
Knowledge of SQL, data pipelines and preparation, structured and unstructured data, feature engineering, data quality and validation, and API and enterprise data integration.
Knowledge of model governance and model risk management, Responsible AI, explainability, validation, bias and fairness, data privacy and security, auditability, documentation, regulatory adherence, and production controls.

Responsibilities

Lead development of cross-functional, multi-platform machine learning software systems.
Design, build, and maintain technology solutions and machine learning models.
Create machine learning algorithms using advanced probability, statistics, and higher-level mathematics.
Build new models and validate existing models.
Perform coding, testing, debugging, documentation, maintenance, and modification of machine learning systems.
Work with Data Scientists to understand prototypes and suggest modifications.
Consult with internal clients to elicit and collect requirements and align technical teams toward delivery.
Design, build, orchestrate, and implement model solutions.
Implement and support production solutions leveraging generative AI, LLMs, and agentic frameworks.
Provide continuous support for models deployed to production applications.
Plan and coordinate releases and provide hands-on support during release windows.
Apply model governance, risk management, regulatory adherence, and responsible AI considerations to solutions.
Use industry expertise to inform decisions and recommendations and stay current on machine learning practices and emerging trends.